Abstract:The constructions of Haar wavelet synopses for large datasets have proven to be useful tools for data approximation. Recently, research on constructing wavelet synopses with a guaranteed maximum error has gained attention. The goal is to find optimal synopses that minimize the approximation error under certain metrics. There are two approaches to realize this goal: size bounded and error bounded. In this paper, we provide a new algorithm for building error-bounded synopses. Our approach is based on the heurist… Show more
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